Using lifecycle configurations with JupyterLab

Lifecycle configurations are shell scripts that are triggered by JupyterLab lifecycle events, such as starting a new JupyterLab notebook. You can use lifecycle configurations to automate customization for your JupyterLab environment. This customization includes installing custom packages, configuring notebook extensions, preloading datasets, and setting up source code repositories.

Using lifecycle configurations gives you flexibility and control to configure JupyterLab to meet your specific needs. For example, you can create a minimal set of base container images with the most commonly used packages and libraries. Then you can use lifecycle configurations to install additional packages for specific use cases across your data science and machine learning teams.